Metallic Landmines Research Articles

Unmanned aerial vehicle for magnetic detection of metallic landmines in military applications

Landmines significantly hinder the rapid movement of military forces and pose major obstacles during critical operations. Unmanned aerial vehicles (UAVs), commonly known as drones, offer several significant advantages for landmine detection. Most studies have tested drone-based systems at controlled sites and demonstrated their effectiveness in limited scenarios, with no examples of using these systems to detect landmines and relay the results to mine clearance machines (MCMs) for actual removal in real military operations. This study evaluated the use of UAVs equipped with magnetometers to detect metallic landmines in military applications. By conducting a series of controlled experiments, the research identified optimal flight conditions—2 m/s flight speed, 1 m survey interval, and 0.5 m sensor altitude—that balance accuracy and operational efficiency. The findings demonstrate that UAV-based magnetometer systems can significantly enhance mine clearance operations by providing near real-time data to MCMs. This approach offers a safer, faster, and more cost-effective alternative to traditional landmine detection methods by addressing the limitations of ground-based operations, such as high risk to human operators and inefficiency.

Design of Unmanned Landmine Detection Vehicle Using Arduino

The concept presented in this article revolves around the idea of an economically designed UGV (Unmanned Ground Vehicle) for the demining process. Landmine discovery is a critical area of exploration due to the high number of losses and injuries caused by landmines each time. The developed technology generally requires human assistance nearby for landmine detection. Every so often the military bear loss of their equipment specialists. Also, false detection leads to the wastage of resources invested in the technology. Our prime aim is to develop a sturdy design for UGV which require minimum assembly time i.e., “Ready-to-Assemble” concept. This paper outlines the design of a UGV equipped with IR sensor, and metal detector and controlled using Arduino microcontroller. The Arduino microcontrollers are programmed to control the UGV’s movements and the sensor data processing. The UGV is built on a six-wheel drive controlled by two motor drivers, which is equipped with IR sensor to detect the heat signature of buried landmines and a metal detector to detect metallic landmines. The GPS location of the landmine is sent directly to a mobile device using a GSM Module. The UGV is controlled using Bluetooth remotely. The design of the vehicle was performed on Solidworks, CAD software. The software was used to design the chassis, suspension system, and other components such as the metal detector and IR sensor mounts. Proteus software was used to stimulate the electronic circuitry. The UGV has potential applications in detecting and removing landmines safely, minimizing the risk of human casualties. Future work could involve improving the system's accuracy and efficiency to make it a more effective tool for landmine detection and removal.

Covariance-guided landmine detection and discrimination using ground-penetrating radar data

ABSTRACTGround penetrating Radar (GPR) can detect and deliver the response signal from any buried kind of object like plastic or metallic landmines, stones, and wood sticks. It delivers three kinds of data: Ascan, Bscan, and Cscan. However, it cannot discriminate between landmines and inoffensive objects or ‘clutter.’ One-class classification is an alternative to detect landmines, especially, as landmines features data are unbalanced. In this article, we investigate the effectiveness of the Covariance-guided One-Class Support Vector Machine (COSVM) to detect, discriminate, and locate landmines efficiently. In fact, compared to existing one-class classifiers, the COSVM has the advantage of emphasizing low variance directions. Moreover, we will compare the one-class classification to multiclass classification to tease out the advantage of the former over the latter as data are unbalanced. Our method consists of extracting Ascan GPR data. Extracted features are used as an input for COSVM to discriminate between landmines and clutter. We provide an extensive evaluation of our detection method compared to other methods based on relevant state of the art one-class and multiclass classifiers, on the well-known MACADAM database. Our experimental results show clearly the superiority of using COSVM in landmine detection and localization.

Towards metal detection and identification for humanitarian demining using magnetic polarizability tensor spectroscopy

This paper presents an inversion procedure to estimate the location and magnetic polarizability tensor of metal targets from broadband electromagnetic induction (EMI) data. The solution of this inversion produces a spectral target signature, which may be used in identifying metal targets in landmines from harmless clutter. In this process, the response of the metal target is modelled with dipole moment and fitted to planar EMI data by solving a minimization least squares problem. A computer simulation platform has been developed using a modelled EMI sensor to produce synthetic data for inversion. The reconstructed tensor is compared with an assumed true solution estimated using a modelled tri-axial Helmholtz coil array. Using some test examples including a sphere which has a known analytical solution, results show the inversion routine produces accurate tensors to within 12% error of the true tensor. A good convergence rate is also demonstrated even when the target location is mis-estimated by a few centimeters. Having verified the inversion routine using finite element modelling, a swept frequency EMI experimental setup is used to compute tensors for a set of test samples representing examples of metallic landmine components and clutter for a broadband range of frequencies (kHz to tens of kHz). Results show the reconstructed spectral target signatures are very distinctive and hence potentially offer an efficient physical approach for landmine identification. The accuracy of the evaluated spectra is similarly verified using a uniform field forming sensor.

Remotely Operated Solar-powered Mobile Metal Detector Robot

This paper presents the development of a remotely operated solar-powered metal detector robot to assist in the humanitarian effort of landmine detection. The deployment of solar-powered metal detector robot on field sites makes the landmine detection system free of the need for refuelling and electrical independence. With the incorporation of inductive sensor, video camera and ATmega32 microcontroller to detect metallic landmines surrounding the suspicious areas. The proposed robotic system was designed and implemented using radio frequency (RF) module for device communication. The robot was tested and analyzed, showing a high-precision metal-detection result, which is competent in robotics applications on landmine sites.

AN EMI INVERSING PROBLEM FOR LANDMINE CHARACTERIZATION BASED ON IMPROVED PARTICLE SWARM OPTIMIZATION AND FINITE ELEMENT ANALYSIS

This paper discusses the characterization of landmine by using the electromagnetic induction technique (EMI). The proposed approach is based on the identiflcation of the physical and geometrical properties of a landmine, from the sensor response. But in such an identiflcation, the inverse problem is unavoidable. At flrst, we begin by simulating the landmine signature by solving a direct problem using the flnite element method which constitutes the direct model. After that, we determine the landmine characteristics by using an inverse model based on a cost function optimization. This model is based on an iterative process which coupling flnite element analysis and Particles Swarm Optimization (PSO). In this step, we apply two PSO techniques: the Standard PSO (SPSO) and the Improved PSO (IPSO), and discuss the problem of local minima of the cost function. The proposed iterative model is applied to determine the conductivity, geometry, and depth of metallic landmine from its signature measured by EMI. The numerical solution gives good results for the identiflcation of landmine.

The Double-peak Characteristic Enhancement Algorithm for Metallic Landmine Detection with UWB SAR

摘要: 机载超宽带合成孔径雷达(UWB SAR)探雷实用化的最大问题是虚警太多，而增强金属地雷双峰特征有助于有效剔除虚警。该文基于双峰显著度最大准则，提出了基于图像域后滤波器的双峰特征增强算法及后滤波器参数优化方法。实测数据处理结果表明，该算法能够有效提高怀疑目标中金属地雷的双峰显著度，从而提高最终金属地雷检测性能。 关键词: 合成孔径雷达 / 地雷检测 / 超宽带 / 双峰特征 / 特征增强

Feature Extraction and Discriminator Design for Landmine Detection on Double-Hump Signature in Ultrawideband SAR

An air- or vehicleborne ultrawideband synthetic aperture radar (UWB SAR) has ground penetrating capability, which provides a sufficient approach to detect landmines over wide areas from a safe standoff distance. In this paper, a support vector machine (SVM) with hypersphere classification boundary, which is referred to as HyperSphere-SVM (HS-SVM), using a hidden Markov model (HMM) kernel on the feature vector extracted by a postfilter-based method is proposed for landmine detection. The postfilter-based method can extract the feature containing not only the amplitude but also the amplitude varying information of the double-hump signature of metallic and plastic landmines. Compared with simple kernels, e.g., the Gaussian kernel, the HMM kernel employs the state-transition information in the extracted feature into the discrimination procedure and, thus, can improve detection performance. The proposed postfilter-based feature extraction method and the HMM kernel HS-SVM are verified on the field data collected by a UWB SAR system in different scenarios.

Detection of metallic and plastic landmines using the GPR and 2-D resistivity techniques

Abstract. Low and non-metallic landmines are one of the most difficult subsurface targets to be detected using several geophysical techniques. Ground penetrating radar (GPR) performance at different field sites shows great success in detecting metallic landmines. However significant limitations are taking place in the case of low and non-metallic landmines. Electrical resistivity imaging (ERI) technique is tested to be an alternative or confirmation technique for detecting the metallic and non-metallic landmines in suspicious cleared areas. The electrical resistivity responses using forward modeling for metallic and non-metallic landmines buried in dry and wet environments utilizing the common electrode configurations have been achieved. Roughly all the utilized electrode arrays can establish the buried metallic and plastic mines correctly in dry and wet soil. The accuracy differs from one array to the other based on the relative resistivity contrast to the host soil and the subsurface distribution of current and potential lines as well as the amplitude of the noises in the data. The ERI technique proved to be fast and effective tool for detecting the non-metallic mines especially in the conductive environment whereas the performances of the other metal detector (MD) and GPR techniques show great limitation.

Radar Detection of Buried Landmines in Field Soils

The contrast in the dielectric constant between a landmine and the surrounding soil is one of the most important parameters to be considered when using ground penetrating radar (GPR) for landmine detection. For most geologic materials the dielectric constant lies within a range of 3 to 30, with dry sand at the lower end of this range at about 3 to 5. Nonmetallic antitank landmines have dielectric constants within a range of about 3 to 10 depending on their composition. A model was developed to predict whether or not field conditions are appropriate for use of GPR instruments. The predictions of this model were validated using GPR profiles in field soils with different soil textures at various soil water contents. Model predictions and field measurements provide convincing evidence that increasing the soil water content around a nonmetallic landmine can improve detection in sand and silt soils. However, data for the clay soils suggest that under elevated soil water conditions detection of nonmetallic landmines are not improved; instead radar images in these soils become worse with increasing soil water content. Data suggest that detection of metallic landmines also degrades with increasing soil water content in sandy soils. The field data are in agreement with the model predictions. Our experimental and model results demonstrate the great potential and the pitfalls of landmine sensors based on GPR. Knowledge of soil texture, dry bulk density, and water content are necessary to determine or predict whether soil conditions are suitable or not for GPR mine detection. The model presented here can be useful for making this determination.

Radar Detection of Buried Landmines in Field Soils

The contrast in the dielectric constant between a landmine and the surrounding soil is one of the most important parameters to be considered when using ground penetrating radar (GPR) for landmine detection. For most geologic materials the dielectric constant lies within a range of 3 to 30, with dry sand at the lower end of this range at about 3 to 5. Nonmetallic antitank landmines have dielectric constants within a range of about 3 to 10 depending on their composition. A model was developed to predict whether or not field conditions are appropriate for use of GPR instruments. The predictions of this model were validated using GPR profiles in field soils with different soil textures at various soil water contents. Model predictions and field measurements provide convincing evidence that increasing the soil water content around a nonmetallic landmine can improve detection in sand and silt soils. However, data for the clay soils suggest that under elevated soil water conditions detection of nonmetallic landmines are not improved; instead radar images in these soils become worse with increasing soil water content. Data suggest that detection of metallic landmines also degrades with increasing soil water content in sandy soils. The field data are in agreement with the model predictions. Our experimental and model results demonstrate the great potential and the pitfalls of landmine sensors based on GPR. Knowledge of soil texture, dry bulk density, and water content are necessary to determine or predict whether soil conditions are suitable or not for GPR mine detection. The model presented here can be useful for making this determination.